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地理加权的随机森林用于宏观水平的碰撞频率预测.
Dongyu Wu1, Yingheng Zhang1, Qiaojun Xiang1
1Jiangsu Key Laboratory of Urban ITS, Southeast University, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, China; School of Transportation, Southeast University, China.
地理加权随机森林 (GWRF) 通过计算空间变化来改善道路安全预测,优于传统的随机森林 (RF) 模型. 这种方法使局部化,有效的道路安全干预成为可能.
科学领域:
- 空间分析是一种空间分析.
- 机器学习在运输中的应用.
- 道路安全工程工程 道路安全工程
背景情况:
- 传统的随机森林 (RF) 模型提供了可靠的预测,但忽视了道路安全的空间变化.
- 宏观层面的道路安全分析需要采用能够捕捉到发生事故频率和风险因素之间的地理不同关系的方法.
研究的目的:
- 引入和评估已修改的随机森林算法,即地理加权随机森林 (GWRF),以改进道路安全分析.
- 将GWRF的预测性能与传统的射频和地理加权回归 (GWR) 模型进行比较.
- 研究空间关系和多线性对道路安全预测准确性的影响.
主要方法:
- 使用伦敦中超输出区域 (MSOA) 数据实现地理加权随机森林 (GWRF) 算法.
- 使用平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 对射频,GWR和GWRF模型进行比较分析.
- 包括区域间差异因素和评估对模型性能多对应性影响的评估.
主要成果:
- 与RF和GWR相比,GWRF表现出优越的预测性能,具有最佳带宽选择.
- 多对线性没有显著影响GWRF模型的准确性,尽管可变重要性值可以减少.
- 解释变量对事故发生频率的影响在不同地区有很大差异,市中心地区受到较小的道路密度的影响,边缘地区受到环境差异的影响.
结论:
- 与传统的RF相比,GWRF为道路安全建模提供了更准确和空间敏感的方法.
- 根据地理特定的风险因素进行的局部道路安全干预措施比全市范围的指导方针更有效.
- 了解风险因素的空间异质性对于制定有针对性和高效的道路安全战略至关重要.
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